Attention Deficit Hyperactivity Disorder (ADHD), known for low attention span and hyperactivity, has been a centre of attention in research and public discourse for a long time (Toplak et al., 2006). The prevalence of people diagnosed with ADHD increases yearly and, notably, there was a significant global increase in ADHD symptoms following the COVID-19 pandemic (Rogers et al., 2023). It is thought that the pandemic lockdowns, which forced people to study and work from home, had negatively affected their attention span.
It this report, I will analyse trends in ADHD medication prescriptions in Scotland by comparing data from the pre-COVID-19 period and post-COVID-19 period. The focus is on the number of prescribed doses, which will allow us to see changes over this period. The aim of this report is to answer the question: Has there been an increase in ADHD medication prescription that could reflect an increase in attention-related challenges following pandemic lockdowns?
The overall aim is to uncover trends in ADHD medication prescription in Scotland pre- and post-COVID-19 lockdowns. The report will show:
There are 5 ADHD medications that have been licensed for the treatment of ADHD in the UK by NHS. These medications are:
# if the file "prescriptions.csv" does not exist in the data folder, create it and save it into the data folder
if (!file.exists(file = here("data", "prescriptions.csv"))) {
json <- fromJSON(file = "https://www.opendata.nhs.scot/api/3/action/package_show?id=prescriptions-in-the-community")
# get all the URLs of the dataframes from the json file
urls <- data.frame(url = unlist(map(json$result$resources, function(resource){resource$url}))) %>%
# filter for dataframes that are set between January 2019 and August 2024
mutate(date = as.numeric(str_extract(url, "/pitc(\\d+)\\.csv$", group = 1))) %>%
filter(date > 201812 & date < 202409)
# read the dataframes from URLs and filter for the 5 types of medications licensed for the treatment of ADHD by the NHS
prescriptions <- lapply(urls$url, function(url){
read_csv(url) %>%
clean_names() %>%
filter(str_detect(bnf_item_description, "ATOMOXETINE|DEXAMFETAMINE|GUANFACINE|LISDEXAMFETAMINE|METHYLPHENIDATE")) %>%
# change the name of the "hbt2014" column into "hbt" in dataframes from the year 2019
rename(any_of(c("hbt" = "hbt2014"))) %>%
mutate(medication_name = word(bnf_item_description, sep = "[ _]"),
dose = word(bnf_item_description, start = 2, end = -1, sep = "[ _]")) %>%
select(hbt, medication_name, dose, number_of_paid_items, paid_quantity, paid_date_month)
})
# join all dataframes into one dataframe
prescriptions <- prescriptions %>%
reduce(full_join)
# save the dataframe into the data file
write_csv(prescriptions, file = here("data", "prescriptions.csv"))
} else {
prescriptions <- read.csv(here("data", "prescriptions.csv"))
}# get a sum of prescribed doses of the 5 types of ADHD medications per month
overview <- prescriptions %>%
mutate(paid_date_month = ym(paid_date_month)) %>%
group_by(medication_name, paid_date_month) %>%
summarise(paid_quantity = sum(paid_quantity))
# create a function that creates a line graph with changeable y axis title, y axis type, and visibility of legend
create_graph <- function(y_title, y_type = NULL, show_legend = FALSE) {
overview %>%
plot_ly(x = ~paid_date_month,
y = ~paid_quantity,
type = "scatter",
mode = "lines",
split = ~medication_name,
color = ~medication_name,
legendgroup = ~medication_name,
showlegend = show_legend) %>%
layout(xaxis = list(title = "Date (month and year)"),
yaxis = list(title = y_title, type = y_type))
}
# create a normal graph and a log graph
overview_normal <- create_graph("Number of prescribed medications")
overview_log <- create_graph("Log of number of prescribed medications", "log", TRUE)
# join the two graphs and tinker with some label title and legend settings
subplot(overview_normal, overview_log, shareX = TRUE, titleX = TRUE, titleY = TRUE, margin = 0.05) %>%
layout(title = "Number of Prescribed Doses of 5 Types of ADHD Medications<br>from January 2019 to August 2024",
hovermode = "x unified",
legend = list(orientation = 'h', y = -0.2, borderwidth = 1, bordercolor = "black", xanchor = "center", x = 0.5))The data indicate that Methylphenidate has shown the highest increase in prescriptions over the years. Although Atomoxetine, Dexamfetamine, and Guanfacine have also seen a steady rise, their growth is not as pronounced as that of Methylphenidate. Notably, in May 2023, prescriptions for Atomoxetine and Dexamfetamine declined, coinciding with the introduction of Lisdexamfetamine to the market. This suggests that Lisdexamfetamine replaced Atomoxetine and Dexamfetamine on the market.
To further explore the increase in ADHD medication prescriptions in Scotland, I will compare the number of prescribed doses before and after the COVID-19 pandemic. This analysis will also account for population differences across Scottish Health Boards. For the pre-COVID comparison, I will use 2019 population data, representing the year just before the first lockdown. For the post-COVID period, 2023 will be the reference year, as complete data for 2024 is not yet available.
# ref: https://www.opendata.nhs.scot/dataset/9f942fdb-e59e-44f5-b534-d6e17229cc7b/resource/652ff726-e676-4a20-abda-435b98dd7bdc/download/hb14_hb19.csv
hb_names <- read_csv(here("data", "hb_names.csv")) %>%
clean_names() %>%
select(hb, hb_name)
# ref: https://www.opendata.nhs.scot/dataset/7f010430-6ce1-4813-b25c-f7f335bdc4dc/resource/27a72cc8-d6d8-430c-8b4f-3109a9ceadb1/download/hb2019_pop_est_14102024.csv
hb_population <- read_csv(here("data", "hb_population.csv")) %>%
clean_names() %>%
filter(sex == "All") %>%
select(year, hb, all_ages)
# ref: "https://maps.gov.scot/ATOM/shapefiles/SG_NHS_HealthBoards_2019.zip"
NHS_healthboards <- st_read(here("data", "NHS_healthboards_2019.shp"))map_data <- prescriptions %>%
mutate(paid_date_month = year(ym(paid_date_month))) %>%
full_join(hb_population, by = c("hbt" = "hb", "paid_date_month" = "year")) %>%
filter(paid_date_month %in% c("2019", "2023"),
!(hbt %in% c("S92000003", "S08000021", "S08000023"))) %>%
group_by(hbt, all_ages, paid_date_month) %>%
summarise(paid_quantity = sum(paid_quantity)) %>%
mutate(ratio = paid_quantity / all_ages) %>%
full_join(NHS_healthboards, by = c("hbt" = "HBCode"))map_figure <- map_data %>%
ggplot() +
geom_sf(aes(fill = ratio, geometry = geometry, text = paste(HBName, "had a ratio of", format(ratio, digits = 2), "in", paid_date_month)), lwd = 0.1) +
scale_fill_distiller(palette = 16, direction = 1) +
theme_void() +
theme(plot.title = element_text(size=11)) +
facet_wrap(~paid_date_month) +
labs(title = "Ratio of Number of Prescribed Doses of ADHD Medication and Health Board Population")
map_figure %>%
ggplotly(tooltip = "text") %>%
style(hoverlabel = list(bgcolor = "white"), hoveron = "fill")Seems like the ratio of population to number of doses of medication is higher in most health boards in Scotland. The only health board that decreased in their number of prescribed doses of ADHD medication is Tayside which went down from 1.02 to 0.904. Grampian has a ratio of 1.33, which is the highest ratio of number of prescribed doses of ADHD medication per person.
past_year_prescriptions <- prescriptions %>%
filter(paid_date_month > 202308 & paid_date_month < 202409) %>%
full_join(hb_names, by = c("hbt" = "hb")) %>%
full_join(hb_population %>% filter(year == "2023"), by = c("hbt" = "hb")) %>%
filter(hb_name == "NHS Lothian") %>%
group_by(medication_name, dose, all_ages) %>%
summarise(paid_quantity = sum(paid_quantity),
number_of_paid_items = sum(number_of_paid_items)) %>%
mutate(paid_quantity = (paid_quantity / all_ages) * 10000,
number_of_paid_items = (number_of_paid_items / all_ages) * 10000) %>%
ungroup()
past_year_prescriptions %>%
select(medication_name, dose, number_of_paid_items, paid_quantity) %>%
group_by(medication_name) %>%
slice_max(paid_quantity, n = 3) %>%
arrange(desc(paid_quantity)) %>%
gt() %>%
tab_header(title = "Top 3 Most Prescribed ADHD Medications of Each Type in The Past Year",
subtitle = "Data from NHS Lothian") %>%
tab_style(style = cell_text(weight = "bold"),
locations = list(cells_title(groups = "title"), cells_row_groups(groups = everything()))) %>%
tab_spanner(label = "Rate per 10k population",
columns = c(number_of_paid_items, paid_quantity)) %>%
tab_style(style = cell_text(style = "italic"),
locations = cells_column_spanners(spanners = everything())) %>%
cols_label(medication_name = "Medication Name",
dose = "Dose",
number_of_paid_items = "Number of Paid Items",
paid_quantity = "Number of Prescriptions") %>%
fmt_number(columns = c(number_of_paid_items, paid_quantity), decimals = 2) %>%
summary_rows(columns = c(number_of_paid_items, paid_quantity),
fns = list("Average" = ~mean(., na.rm = TRUE)),
fmt = list(~ fmt_number(., decimals = 2))) %>%
grand_summary_rows(columns = c(number_of_paid_items, paid_quantity),
fns = list("Overall Average" = ~mean(., na.rm = TRUE)),
fmt = list(~ fmt_number(., decimals = 2))) %>%
opt_row_striping()| Top 3 Most Prescribed ADHD Medications of Each Type in The Past Year | |||
| Data from NHS Lothian | |||
| Dose |
Rate per 10k population
|
||
|---|---|---|---|
| Number of Paid Items | Number of Prescriptions | ||
| METHYLPHENIDATE | |||
| 10MG TABLETS | 50.06 | 3,466.82 | |
| 5MG TABLETS | 37.28 | 2,327.26 | |
| 20MG TABLETS | 15.41 | 1,099.76 | |
| Average | — | 34.25 | 2,297.95 |
| DEXAMFETAMINE | |||
| 5MG TABLETS | 15.19 | 1,560.51 | |
| 5MG/5ML ORAL SOLUTION SUGAR FREE | 0.22 | 34.27 | |
| 5MG/5ML ORAL LIQUID | 0.02 | 4.90 | |
| Average | — | 5.14 | 533.23 |
| ATOMOXETINE | |||
| 40MG CAPSULES | 7.62 | 287.83 | |
| 60MG CAPSULES | 5.16 | 205.38 | |
| 10MG CAPSULES | 3.45 | 191.70 | |
| Average | — | 5.41 | 228.30 |
| LISDEXAMFETAMINE | |||
| 30MG CAPSULES | 0.05 | 2.13 | |
| 40MG CAPSULES | 0.05 | 1.52 | |
| 50MG CAPSULES | 0.02 | 0.61 | |
| Average | — | 0.04 | 1.42 |
| GUANFACINE | |||
| 1MG MODIFIED-RELEASE TABLETS | 0.01 | 0.61 | |
| 3MG MODIFIED-RELEASE TABLETS | 0.01 | 0.61 | |
| Average | — | 0.01 | 0.61 |
| Overall Average | — | 9.61 | 655.99 |
Here will be a conclusion soon.
Rogers, M.A. and MacLean, J. (2023) ‘ADHD symptoms increased during the COVID-19 pandemic: A meta-analysis’, Journal of Attention Disorders, 27(8), pp. 800–811. doi:10.1177/10870547231158750.
Toplak, M.E., Dockstader, C., Tannock, R. (2006) ‘Temporal information processing in ADHD: Findings to date and New Methods’, Journal of Neuroscience Methods, 151(1), pp. 15–29. doi:10.1016/j.jneumeth.2005.09.018.